MCP Order Flow Server
@fintools-ai
About MCP Order Flow Server
This is the mcp to fetch and get order flow information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-order-flow-server": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}Tools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is MCP Order Flow Server?
MCP Order Flow Server provides real-time order flow analysis for options trading decisions. It consumes quote data from Redis and delivers structured analysis through MCP tools, including momentum indicators, size dynamics, pattern detection, support/resistance levels, and market behavior flags.
How to use MCP Order Flow Server?
Install dependencies, configure Redis environment variables (REDIS_HOST, REDIS_PORT, etc.), and run the server via python -m src.mcp_server or the dev_start.sh script. The primary tool is analyze_order_flow(ticker, history, include_patterns), which returns XML-formatted order flow data.
Key features of MCP Order Flow Server
- Real-time momentum indicators (bid/ask lifts, drops, price changes)
- Size dynamics analysis (volume patterns, large order detection)
- Pattern detection (absorption, stacking, sweeps, momentum shifts)
- Support/resistance level identification from order flow
- Market behavior flags (YES/NO) for LLM interpretation
- Background processing every 1 second for updated metrics
Use cases of MCP Order Flow Server
- Enabling LLM agents to make informed options trading decisions
- Detecting institutional accumulation or distribution via absorption patterns
- Identifying trend initiation or reversal through momentum shifts
- Recognizing aggressive execution or stop hunting with sweep detection
- Providing structured order flow context for automated trading assistants
FAQ from MCP Order Flow Server
What are the runtime dependencies?
Python 3.10 or later, a running Redis server, and the mcp-trading-data-broker publishing quotes into Redis.
How is the data stored and for how long?
Raw quotes and detected patterns are stored in Redis with a default TTL of 3600 seconds. Calculated metrics have TTLs ranging from 60 to 600 seconds depending on the window.
What patterns does the server detect?
It detects absorption, stacking, momentum shifts, and sweeps. Each pattern has specific trigger conditions defined in the README (e.g., >15k size change for sweeps).
How do I configure the server?
Set environment variables such as REDIS_HOST, REDIS_PORT, REDIS_DB, LOG_LEVEL, and PROCESSOR_INTERVAL (default 1 second). A Claude Desktop configuration example is provided for integration.
Is authentication or authorization supported?
The README does not mention any authentication or authorization mechanisms for the MCP server or its Redis connection. An optional REDIS_PASSWORD environment variable exists for Redis authentication.
Frequently asked questions
What are the runtime dependencies?
Python 3.10 or later, a running Redis server, and the `mcp-trading-data-broker` publishing quotes into Redis.
How is the data stored and for how long?
Raw quotes and detected patterns are stored in Redis with a default TTL of 3600 seconds. Calculated metrics have TTLs ranging from 60 to 600 seconds depending on the window.
What patterns does the server detect?
It detects absorption, stacking, momentum shifts, and sweeps. Each pattern has specific trigger conditions defined in the README (e.g., >15k size change for sweeps).
How do I configure the server?
Set environment variables such as `REDIS_HOST`, `REDIS_PORT`, `REDIS_DB`, `LOG_LEVEL`, and `PROCESSOR_INTERVAL` (default 1 second). A Claude Desktop configuration example is provided for integration.
Is authentication or authorization supported?
The README does not mention any authentication or authorization mechanisms for the MCP server or its Redis connection. An optional `REDIS_PASSWORD` environment variable exists for Redis authentication.
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